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From Meethu Mathew <meethu.mat...@flytxt.com>
Subject [MLlib] Contributing algorithm for DP means clustering
Date Thu, 18 Jun 2015 04:58:11 GMT
Hi all,

At present, all the clustering algorithms in MLlib require the number of
clusters to be specified in advance.

The Dirichlet process (DP) is a popular non-parametric Bayesian mixture
model that allows for flexible clustering of data without having to specify
apriori the number of clusters. DP means is a non-parametric clustering
algorithm that uses a scale parameter 'lambda' to control the creation of
new clusters.

We have followed the distributed implementation of DP means which has been
proposed in the paper titled "MLbase: Distributed Machine Learning Made
Easy" by Xinghao Pan, Evan R. Sparks, Andre Wibisono.

I have raised a JIRA ticket at
https://issues.apache.org/jira/browse/SPARK-8402

Suggestions and guidance are welcome.

Regards,

Meethu Mathew
Senior Engineer
Flytxt
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